Showing 22 open source projects for "python tools"

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  • 1
    DI-engine

    DI-engine

    OpenDILab Decision AI Engine

    DI-engine is a unified reinforcement learning (RL) platform for reproducible and scalable RL research. It offers modular pipelines for various RL algorithms, with an emphasis on production-level training and evaluation.
    Downloads: 0 This Week
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  • 2
    highway-env

    highway-env

    A minimalist environment for decision-making in autonomous driving

    HighwayEnv is an OpenAI Gym-compatible environment focused on autonomous driving scenarios. It provides flexible simulations for testing decision-making algorithms in highway, intersection, and merging traffic situations.
    Downloads: 0 This Week
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  • 3
    RL Baselines3 Zoo

    RL Baselines3 Zoo

    Training framework for Stable Baselines3 reinforcement learning agents

    rl-baselines3-zoo is a collection of pre-trained models, benchmarks, and hyperparameter tuning tools built on top of Stable Baselines3, a reinforcement learning library. It provides an easy way to test, evaluate, and train RL agents across a wide variety of environments.
    Downloads: 0 This Week
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  • 4
    TensorHouse

    TensorHouse

    A collection of reference Jupyter notebooks and demo AI/ML application

    TensorHouse is a scalable reinforcement learning (RL) platform that focuses on high-throughput experience generation and distributed training. It is designed to efficiently train agents across multiple environments and compute resources. TensorHouse enables flexible experiment management, making it suitable for large-scale RL experiments in both research and applied settings.
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  • 5
    LightZero

    LightZero

    [NeurIPS 2023 Spotlight] LightZero

    LightZero is an efficient, scalable, and open-source framework implementing MuZero, a powerful model-based reinforcement learning algorithm that learns to predict rewards and transitions without explicit environment models. Developed by OpenDILab, LightZero focuses on providing a highly optimized and user-friendly platform for both academic research and industrial applications of MuZero and similar algorithms.
    Downloads: 0 This Week
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  • 6
    PettingZoo

    PettingZoo

    An API standard for multi-agent reinforcement learning environments

    PettingZoo is a standardized API and library for multi-agent reinforcement learning (MARL) environments. It provides a broad set of environments and tools to facilitate the development and evaluation of multi-agent algorithms.
    Downloads: 0 This Week
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  • 7
    VectorizedMultiAgentSimulator (VMAS)

    VectorizedMultiAgentSimulator (VMAS)

    VMAS is a vectorized differentiable simulator

    VectorizedMultiAgentSimulator is a high-performance, vectorized simulator for multi-agent systems, focusing on large-scale agent interactions in shared environments. It is designed for research in multi-agent reinforcement learning, robotics, and autonomous systems where thousands of agents need to be simulated efficiently.
    Downloads: 0 This Week
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  • 8
    RWARE

    RWARE

    MuA multi-agent reinforcement learning environment

    robotic-warehouse is a simulation environment and framework for robotic warehouse automation, enabling research and development of AI and robotic agents to manage warehouse logistics, such as item picking and transport.
    Downloads: 0 This Week
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  • 9
    Tensorforce

    Tensorforce

    A TensorFlow library for applied reinforcement learning

    Tensorforce is an open-source deep reinforcement learning framework built on TensorFlow, emphasizing modularized design and straightforward usability for applied research and practice.
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  • 10
    Stable Baselines3

    Stable Baselines3

    PyTorch version of Stable Baselines

    Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which...
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  • 11
    Agent S2

    Agent S2

    Agent S: an open agentic framework that uses computers like a human

    Simular's Agent S2 represents a leap forward in the development of computer-use agents, capable of autonomously interacting with a range of devices and interfaces. By integrating specialized AI models, Agent S2 delivers state-of-the-art performance, whether on desktop systems or smartphones. Through modular architecture, it efficiently handles complex tasks, such as navigating UIs, performing low-level actions like text selection, and executing high-level strategies like planning....
    Downloads: 0 This Week
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  • 12
    Habitat-Lab

    Habitat-Lab

    A modular high-level library to train embodied AI agents

    ... and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics.
    Downloads: 0 This Week
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  • 13
    EasyRL

    EasyRL

    Reinforcement learning (RL) tutorial series

    easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts.
    Downloads: 0 This Week
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  • 14
    ElegantRL

    ElegantRL

    Massively Parallel Deep Reinforcement Learning

    ElegantRL is an efficient and flexible deep reinforcement learning framework designed for researchers and practitioners. It focuses on simplicity, high performance, and supporting advanced RL algorithms.
    Downloads: 0 This Week
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  • 15
    SLM Lab

    SLM Lab

    Modular Deep Reinforcement Learning framework in PyTorch

    SLM Lab is a modular and extensible deep reinforcement learning framework designed for research and practical applications. It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently.
    Downloads: 0 This Week
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  • 16
    gym-pybullet-drones

    gym-pybullet-drones

    PyBullet Gymnasium environments for multi-agent reinforcement

    Gym-PyBullet-Drones is an open-source Gym-compatible environment for training and evaluating reinforcement learning agents on drone control and swarm robotics tasks. It leverages the PyBullet physics engine to simulate quadrotors and provides a platform for studying control, navigation, and coordination of single and multiple drones in 3D space.
    Downloads: 0 This Week
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  • 17
    Stable Baselines

    Stable Baselines

    A fork of OpenAI Baselines, implementations of reinforcement learning

    Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new...
    Downloads: 0 This Week
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  • 18
    RL Baselines Zoo

    RL Baselines Zoo

    A collection of 100+ pre-trained RL agents using Stable Baselines

    RL Baselines Zoo is a comprehensive training framework and collection of pre-trained RL agents using Stable-Baselines3. It offers tools for training, tuning, and evaluating RL algorithms across many standard environments, including MuJoCo, Atari, and robotics simulations. Designed for reproducible RL research and benchmarking, it includes scripts, hyperparameter presets, and best practices for training robust agents.
    Downloads: 0 This Week
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  • 19
    ChainerRL

    ChainerRL

    ChainerRL is a deep reinforcement learning library

    ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments...
    Downloads: 0 This Week
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  • 20
    SMAC

    SMAC

    SMAC: The StarCraft Multi-Agent Challenge

    SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL.
    Downloads: 0 This Week
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  • 21
    Easy-TensorFlow

    Easy-TensorFlow

    Simple and comprehensive tutorials in TensorFlow

    The goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format). There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a simple path to learn TensorFlow. In addition to the...
    Downloads: 0 This Week
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  • 22
    A Python class library of tools for learning agents, including reinforcement learning algorithms, function approximators, and vector quantizations algorithms. (Pronounced "plastic".)
    Downloads: 0 This Week
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